Project Abstract/Summary Ultra-low dose CT, defined as sub-millisievert (sub-mSv) imaging of the entire chest, abdomen or pelvis, is critically needed for healthcare of patients with chronic diseases and cancer. Unfortunately, photon starvation and electronic noise make imaging at such dose levels challenging. Photon starvation refers to the number of transmitted photons. When no photons are transmitted, the measurement is essentially useless. If few photons are transmitted, the measurement carries information, but its interpretation and value are confounded by electronic noise. Solutions with encouraging results have been offered for sub-mSv chest imaging, but these are not widely available and not easily generalizable across anatomical sites, vendors and scanner models. We propose a novel, robust solution for ultra-low dose CT that will overcome these issues. We refer to our solution as FAIR-CT, which stands for Finite-Angle Integrated-Ray CT. FAIR-CT operates under the principle that photon starvation and the confounding effect of electronic noise are best handled by avoiding them, which is made possible by increasing the data integration time during the source-detector rotation. FAIR-CT data strongly deviate from the classical CT data model and share the streak artifact problem of sparse view sampling. FAIR-CT data acquisition also affects azimuthal resolution. We anticipate that these issues can be suitably handled using advanced image reconstruction techniques. Once available, FAIR-CT will allow improvements in longitudinal monitoring of patients with chronic diseases such as COPD, urolithiasis and diabetes, thereby reducing mortality and co-morbidities. FAIR-CT will also allow advancing cancer therapy treatments by enabling adjustments in radiation therapy plans between dose fractions without increasing CT radiation exposure, and by facilitating early detection of inflammations in drug-based therapies. To bring FAIR-CT towards fruition, we will work on two specific aims: (1) Creation of a comprehensive collection of FAIR-CT data sets enabling rigorous development, validation and evaluation of image reconstruction algorithms; (2) Development, validation and evaluation of advanced image reconstruction algorithms. The FAIR-CT data sets will involve the utilization of state-of-the-art scanners and include real patient data synthesized from high dose scans acquired for standard of care. Two complementary image reconstruction approaches will be investigated. Namely, model-based iterative reconstruction with non-linear forward model and dedicated compressed sensing regularization; and deep learning-based refinement of FBP reconstructions using target images with task-adapted image quality. Image quality evaluation will account for critical biological variables and involve objective metrics such as structure similarity and contrast-to-noise ratio for clinically-proven lesions, as well as task-based performance metrics involving human readers.